Automated thresholding for unsupervised neurofeedback sessions

ABSTRACT

Disclosed is a computer-implemented method for biofeedback training of a subject, the method including iteratively obtaining a series of biomarkers, each biomarker being representative of a bio-signal of the subject on a first time window; computing an intermediate threshold Thrintermediate(t) based on the series of biomarkers on a second time window, such that the intermediate threshold on the second time window could provide the subject with an expected reward ratio; computing a threshold Thr(t) as the weighted sum of the intermediate threshold Thrintermediate(t) and the threshold of the previous iteration Thr(t−1) and reporting in real-time a reward to the subject based on the difference between the biomarker and the computed threshold Thr(t), wherein at each iteration the time windows are moved forward in time. Also disclosed is a system for implementing the method.

FIELD OF INVENTION

The present invention pertains to the field of neurofeedback. Morespecifically, the invention relates to a system and a method forunsupervised adjustment of a threshold during a neurofeedback session.

BACKGROUND OF INVENTION

Neurofeedback is a biofeedback training which involves self-regulationof ongoing brain activity by sensory feedback. Neurofeedback may be usedfor various purposes, such as for instance for improving focus andattention.

Changes made in the desired direction are rewarded in a way that isunderstandable by a non-specialist, for instance with particular tonesor pictures. On the contrary, negative feedback may be provided forundesirable or deviant brain activity.

In order to provide efficient neurofeedback training, i.e. to enable thesubject to gain control of the brain activity and to train it in thedesired direction, the threshold between a positive and negative rewardmust be finely tuned. Indeed, threshold adjustment stimulates thesubjects and maintains engagement to the session.

The actual clinical practice of neurofeedback supposes trainersupervision with manual selection of the threshold during the session.The subject's resting-state brain activity is recorded for few seconds,in order to calibrate the algorithms. Then the subject performs thetraining session. During sessions, his brain activity is recorded, forinstance by electroencephalography (EEG) on several channels (also knownas electrodes), while he performs a task. A value representative of theEEG signals (in some specific frequency ranges) is then computed. It isintegrated over a time window (called “integration window”), which istaken every ε seconds (potentially overlapping time windows), andaveraged over all electrodes (see FIG. 1). Said measure of activity iscalled hereafter a neuromarker and the consecutive integration overwindows leads to a neuromarker time-series of frequency 1/ε.

The extracted neuromarker modulates a feedback that is played (auditoryfeedback) or displayed (visual feedback) to the subject, and thatreflects the neuromarker variation. A threshold is applied on theneuromarkers so that the subject is rewarded when his activity is belowor above the threshold, depending on the protocol (e.g. downtrainingprotocol rewards the subject if the neuromarker is below the thresholdand uptraining protocol rewards the subject if the neuromarker is abovethe threshold). The specialist changes the threshold manually, adjustingon the subject's performance evolution and accounting for itsvariability. The purpose of the manual adjustment is to maintain areward ratio that stimulates the subjects and maintain engagement: itshould be neither too easy (subject is bored) nor too difficult (subjectis discouraged). It is sometimes claimed that the “art” of neurofeedbackresides in the practitioner's ability to adjust these thresholds valuesto maintain the right levels of engagement and challenge and therebymaximize efficacy.

Consequently, neurofeedback sessions have to be performed by the subjectwith assistance of a trained specialist. Said supervision considerablylimits availability of neurofeedback sessions. Moreover, it also limitsfeasibility studies and repeatability.

Unsupervised automatic adjustment of the threshold has also beenimplemented. Existing automatic thresholding systems are implementedbased on a moving average time window. A time window, that generallylengths few seconds (also named “estimation time window”), moves forwardwith a regular pace of δ seconds (potentially overlapping time windows).On every window, a threshold is computed (see FIG. 1) such that itprovides rewards at an expected reward rate (also referred to as“expected reward ratio”). Said type of threshold has three majordrawbacks: first, it is strongly influenced by artefacts in the EEGsignal at all times. Second, a way-too-adaptive threshold does not allowthe subject to learn and progress. With such automatic adjustments, thereward ratio is maintained at a constant rate whatever the actualneuromarker value, thereby hindering the conditioning and ultimately theself-modulation process that mediates the therapeutic or non-therapeuticeffect. Finally, such thresholding does not mime specialist's habits whoupdate the threshold only when subject's performances are too far fromthe expected reward ratio.

For example, Lansbergen et al. discloses reward threshold levelsadjusted automatically based on the digitally filtered real-time EEGsignal every 30 s so that the subject was rewarded about 80% of the time(Lansbergen et al., ADHD and EEG-neurofeedback: a double-blindrandomized placebo-controlled feasibility study, Journal of neuraltransmission, 2010. DOI: 10.1007/s00702-010-0524-2). However, theanalysis of Lansbergen study did not reveal significant differencesbetween EEG-neurofeedback training group and placebo feedback group,wherein the feedback is not related to the brain activity but to asimulated EEG signal.

Consequently, automatic adjusted reward threshold according to the priorart does not work as effective as manually adjusted reward threshold.

There is therefore a need for a new method and system, whereinneurofeedback sessions can be performed by the subject without thesupervision of a trainer with a preserved efficacy, the method andsystem being such that threshold adjustment imitates trainer practice.

Definitions

In the present invention, the following terms have the followingmeanings:

-   -   When the term “about” is used in conjunction with a numerical        range, it modifies that range by extending the boundaries above        and below the numerical values set forth. In general, the term        “about” is used herein to modify a numerical value above and        below the stated value by a variance of 10 percent, preferably        of 5 percent.    -   “Biofeedback training” refers to a training which involves        self-regulation of a biomarker by sensory feedback.    -   “Bio-signal” refers herein to any signal in subjects that can be        continually measured and monitored. Bio-signal includes        non-limitatively neural-signal, photopletismogram, arterial and        venous blood oxygen saturation, arterial and venous blood        pressure, heart rate, temperature, respiratory parameters        including non-limitatively respiratory rate, tidal volume, rapid        shallow breathing and respiratory variability. Bio-signal refers        especially to any biological parameter that can be measured by        an instrument that converts a physical measure (light, pressure,        electricity, radio-signal . . . ) into an analogous signal (in        volts) and which is then digitalised every ε, wherein ε is the        sampling period, so that the biological signal is a digital        univariate or multivariate time series at frequency 1/ε.    -   “EEG artefacts” are recorded signals that are non-cerebral in        origin. They arise from different sources: external electrical        interference, movement of the head, muscular contractions,        blinks, etc.    -   “Electroencephalography” (EEG) refers to an electrophysiological        monitoring method to record electrical brain activity. In        non-invasive EEG, electrodes are placed along the scalp.    -   “Estimation time window” refers to a moving average time window        used to compute the intermediate threshold and infer the reward        percentage on said window.    -   “Growth rate (r)” refers to a predefined coefficient which        defines the slope of the logistic model of the coefficient α.    -   “Learning coefficient (k)” refers to a predefined coefficient        which defined the asymptote of the logistic model of the        coefficient α.    -   “Neural signal” refers herein to the signal obtained by        measuring neural activity. Said neural activity may be measured        by: deep brain electrodes; electrocorticography (ECoG);        electroencephalography (EEG); magnetoencephalography (MEG);        magnetic resonance imaging (MRI): diffusion MRI, perfusion MRI,        functional MRI (fMRI); near-infrared spectroscopy (NIRS);        positron emission tomography (PET); or        stereoelectroencephalography (SEEG).    -   “Biomarker” or “Neuromarker” refers to a value representative of        a biological signal which is trained. Said values are integrated        over a time window, which is taken every δ second (potentially        overlapping time windows) leading to a biomarker time series of        frequency 1/δ. When applied for neurofeedback, the neuromarker        refers to a specific neural activity trained during        neurofeedback sessions at specific locations on the scalp. For        EEG neurofeedback, the neuromarker corresponds to energy levels,        or ratio of energy levels, computed within specific frequency        bands: alpha (8-12 Hz), beta (12-30 Hz), theta (4-8 Hz) and        sensorimotor rhythms (12-15 Hz). For instance, the neuromarker        is the power within two frequencies at a specific location, the        power within two frequencies at a specific location divided by        the power within another frequency band at a specific location,        the power within two frequencies at a specific location divided        by the power within a larger frequency band centered at the        frequency of interest at the specific location, the power within        two frequencies at a specific location divided by the signal        power integrated across all frequencies or the first derivative        of any of the above neuromarker.    -   “Real-time” means that the latency between the bio-signal record        and the feedback display is shorter enough to be unnoticed by        the subject. Said latency is generally less than a few seconds,        or less than 1 second, or less than 500 ms or less than 100 ms.    -   “Resting-state bio-signal” refers to a bio-signal activity        recorded while a subject is not performing any task, just        resting.    -   “Reward”: see threshold. The reward may be any sensory reward        (e.g. specific tones or pictures).    -   “Reward ratio” refers to the percentage of reward received by        the subject during a fixed period of time.    -   “Expected Reward ratio” refers to a predefined reward ratio.    -   “Reward ratio tolerance” refers to a tolerable percentage        deviance around the expected reward ratio.    -   “Reward ratio tolerance time window” refers to a duration during        which a deviance from the expected reward ratio is tolerated.    -   “Threshold” refers to a reference value to which the biomarker        is compared. For instance, a down training protocol rewards the        subject when the biomarker is maintained below the threshold        during a predefined duration (see time-gating and time        boosting). An up training protocol rewards the subject when the        biomarker is maintained above the threshold during a predefined        duration.    -   “Time boosting” refers to the larger time window during which        the biomarker has to be maintained below the threshold (for a        down training protocol) or above the threshold (for an up        training protocol) in order to get an extra reward.    -   “Time gating” refers to the lowest time spend below the        threshold (for a down training protocol) or above the threshold        (for an up training protocol) in order to a get a reward.

DESCRIPTION

In a first aspect, this invention aims at proposing acomputer-implemented method for biofeedback training of a subject. Saidmethod comprising iteratively:

-   -   obtaining a series of biomarkers, each biomarker being        representative of a bio-signal of the subject on a first time        window;    -   computing an intermediate threshold Thr_(intermediate(t)) based        on the series of biomarkers on a second time window, such that        said intermediate threshold on said second time window could        provide the subject with an expected reward ratio;    -   computing a threshold Thr_((t)) as the weighted sum of the        intermediate threshold Thr_(intermediate(t)) and the threshold        of the previous iteration Thr_((t−1)); and    -   reporting in real-time a reward to the subject based on the        difference between the biomarker and the computed threshold        Thr_((t));    -   wherein at each iteration the time windows are moved forward in        time.

In one embodiment, the first time window used for obtaining a biomarkeris referred to as the integration time window. According to oneembodiment, the integration time window ranges from 0.1 second to 5seconds, preferably from 0.5 second to 2 seconds. According to oneembodiment, the integration time window is a moving overlapping window.According to one embodiment, the integration time window is a movingnon-overlapping window. According to one embodiment, at each iterationthe integration time window is moved forward in time with a pace ε,preferably a regular pace.

In one embodiment, the second time window used for computing theintermediate threshold Thr_(intermediate(t)) is referred to as theestimation time window. According to one embodiment, the estimation timewindow ranges from few seconds to several tens of seconds, preferablyfrom 2 seconds to 10 seconds. According to one embodiment, theestimation time window is a moving overlapping window. According to oneembodiment, the estimation time window is a moving non-overlappingwindow. According to one embodiment, at each iteration the estimationtime window is moved forward in time with a pace δ, preferably a regularpace. In one embodiment, the pace of the estimation time window δdiffers from the pace of the integration time window ε.

In one embodiment, the reward reported in real-time to the subject isbased on the difference between the current biomarker at time (t) andthe computed threshold Thr_((t)) at time (t).

According to one embodiment, the expected reward ratio ranges from 10%to 100%.

According to one embodiment, the sum of the weighting factors is equalto 1.

According to one embodiment, the threshold is computed as follows:Thr _((t)) =α*Thr _((t−1))+(1−α)*Thr _(intermediate(t))wherein α is a constant or variable coefficient and ranges between 0and 1. According to a preferred embodiment, the coefficient α rangesstrictly between 0 and 1. According to one embodiment, α is not equal to0 or 1.

Computing the threshold as explained above limits the possible values ofThr_((t)) between bounded values and prevents unwanted growth of thevalues of Thr_((t)).

If α equals to 0, the computed threshold Thr_((t)) matches the movingaverage automated threshold computed on the estimation time windowThr_((t))=Thr_(intermediate(t)). If α equals to 1, the computedthreshold Thr_((t)) reaches a plateau and the threshold at time t equalsthe threshold at time (t−1): Thr_((t))=Thr_((t−1)). The closer α is to1, the more the weight of the threshold's history Thr_((t−1)) increasesleading to computed threshold that takes past values into account. Also,as a increases the threshold progressively converges towards a constantvalue.

According to one embodiment, the coefficient α follows a logistic model,preferably a sigmoid model. According to one embodiment, the coefficientα is variable and

${\alpha_{t} = {\alpha_{t - 1} + {{r.\alpha_{t - 1}}*\left( \frac{k - \alpha_{t - 1}}{k} \right)}}};$wherein k is a learning coefficient and r a growth rate.

The coefficient α is used to compute a threshold that adapts from dataand stabilizes as the threshold approaches its limit. As α increases allthe way to 1, the weight of the threshold's history Thr_((t−1))increases leading to computed threshold that takes past values intoaccount. Also, as a increases the threshold progressively convergestowards a constant value.

According to one embodiment, α₀ ranges from 0 to 1. According to oneembodiment, α₀ ranges strictly between 0 and 1.

According to said embodiment, the coefficient α is bounded between theinitial value of α (i.e. α₀) and k, the learning coefficient.Furthermore, α converges towards k with a convergence speed that dependson the growth rate r.

k denotes the maximum values of the coefficient α, i.e. the modelasymptote. According to one embodiment, k ranges from 0 to 1. For kequals to 1, the coefficient α converges towards 1 where the learningstops and α_(t)=α_(t−1).

r defines the slope of the coefficient α. The higher is r, the faster isthe convergence towards an asymptotic value of α. According to oneembodiment, r ranges from 0 to 1.

This behaviour is illustrated in FIG. 3. The evolution of α, of thethreshold computed on the previous iteration Thr_((t−1)), of theintermediate threshold Thr_(intermediate(t)) and of the thresholdThr_((t)) are depicted over time. In said exemplary embodiment, α₀ isequal to 0.8.

Initial values of Thr_((t)) are computed from a previous session(calibration);

Thr_(intermediate(t)) is computed on an estimation time window of 5δseconds. As long as this time has not elapsed, the threshold Thr_((t))cannot be computed and is initialized at a predefined value. First valueis computed at time 6δ;

At time 6δ, a value Thr_(intermediate(t)) has been computed on theestimation time window leading to an update of the Thr_((t)) value;

Thr_((t−1)) equals the Thr_((t)) at time δ−1;

Thr_((t)) value is computed from the previous iteration according to theformula of the present invention;

When alpha reaches 1, Thr_((t)) values equals to Thr_((t−1)), and thethreshold is constant and maintained over time (the boxed numbers referto FIG. 3).

According to one embodiment, the coefficient α is reset to a predefinedinitial value α₀ if the reward ratio computed during a third time windowdeparts from more than a reward ratio tolerance around the expectedreward ratio.

In one embodiment, the third time window used for resetting α isreferred to as the reward ratio tolerance time window. According to oneembodiment, the reward ratio tolerance time window ranges from 0 secondand the session duration, preferably from 10 seconds to 1 minute.

According to one embodiment, the reward ratio tolerance ranges between0% (no tolerance, the threshold resets every time the reward ratiocomputed on the estimation time window is not exactly equal to theexpected reward ratio) to 100% (large tolerance, the reward ratio cantake any value without inducing a threshold reset).

According to one embodiment, as depicted in FIG. 2, a first reward isreported to the subject if the subject maintained the biomarker abovethe computed threshold Thr_((t)) during a time longer than a time gatingparameter (for an up training protocol). For a down training protocol, afirst reward is reported to the subject if the subject maintained thebiomarker below the computed threshold Thr_((t)) during a time longerthan a time gating parameter.

So when the biomarker is trained towards a defined direction, eitherabove or below the computed threshold Thr_((t)), a first reward isreported to the subject if the subject maintained the biomarkerrespectively above or below the computed threshold Thr_((t)) during atime longer than a time gating parameter. Said time gating prevents fromrewarding transitory artefacts. According to one embodiment, the timegating is below 1 second, preferably ranging from 100 to 600milliseconds.

According to one embodiment, as depicted in FIG. 2, a second reward isreported to the subject if the subject maintained the biomarker abovethe computed threshold Thr_((t)) during a time longer than a timeboosting parameter (for an up training protocol). For a down trainingprotocol, a second reward is reported to the subject if the subjectmaintained the biomarker below the computed threshold Thr_((t)) during atime longer than a time boosting parameter.

Consequently, when the biomarker is trained towards a defined direction,either above or below the computed threshold Thr_((t)), a second rewardis reported to the subject if the subject maintained the biomarkerrespectively above or below the computed threshold Thr_((t)) during atime longer than a time boosting parameter. According to one embodiment,the time boosting is longer than the time gating. According to oneembodiment, the time boosting is ranging from 1 to 5 seconds.

According to one embodiment, the initial values of the threshold arecomputed from a previous session or from a series of biomarkers computedfrom a bio-signal obtained under a given condition. Said bio-signalobtained under a given condition is for instance a resting statebio-signal.

According to one embodiment, the method according to the presentinvention further comprises the step of removal of artefacts from thebio-signal of the subject before computing the biomarker representativeof the bio-signal on the first time window. Said removal of artefactsmay be performed using any real-time artefact removal algorithms knownby one skilled in the art.

According to one embodiment, the method is a computer-implementedmethod.

According to one embodiment, the method is a non-therapeutic method.According to said embodiment, the method may be used to improve theskills of a subject, such as for instance precision.

In a second aspect, this invention also aims at proposing a method forautomated initialization of the parameters required for implementing themethod for biofeedback training of a subject according to the invention,wherein the method comprises the following steps:

-   -   obtaining a series of biomarkers of a subject from a previous        session;    -   obtaining a series of threshold manually chosen by an operator        or trainer during said previous session; and    -   identifying the optimal set of parameters that minimize an error        function between the manual threshold and the threshold        Thr_((t)) computed from the same series of biomarkers with the        method according to the invention.

According to one embodiment, said method for automated initialization ofthe parameters is implemented subjectwise, on a population, or fordifferent trainers.

According to one embodiment, the method for automated initialization ofthe parameters comprises:

-   -   selecting randomly i couples of data (r, k, α₀)_(i) wherein i is        large enough, preferably i ranges from 1 to 100 000, from 1 to        10 000 or from 1 to 1000;    -   computing for each couple of data the threshold according to the        present invention on the series of biomarkers;    -   computing an error function between the obtained threshold and        the manual threshold; and    -   selecting the couple of data i with the lower error.

According to one embodiment, said method is also implemented forobtaining the following parameters: the expected reward ratio, the timegating, the time boosting, the estimation time window, the reward ratiotolerance and the reward ratio tolerance time window.

According to exemplary embodiment, the error function is a method ofleast squares.

According to one embodiment, said method is a computer-implementedmethod.

In a third aspect, this invention aims at proposing a system forimplementing the method according to the first aspect of the invention.Especially, said aspect relates to a system comprising means forcarrying out the method according to the first aspect.

Optionally, the invention aims to provide a computer program comprisinginstructions which, when the program is executed by a computer, causethe computer to carry out the method according to the first aspect ofthe present invention.

In another alternative, the invention aims to provide acomputer-readable medium comprising instructions which, when executed bya computer, cause the computer to carry out the method of the firstaspect of the present invention.

In one embodiment, said system for biofeedback training of a subjectcomprises:

-   -   at least one sensor for obtaining a bio-signal of the subject;    -   a computing unit for computing a series of biomarkers, each        biomarker being representative of the bio-signal of the subject        on a first time window; and    -   a memory comprising the series of biomarkers and a set of        parameters including an expected reward ratio and an initial        threshold Thr₍₀₎;    -   at least one interacting means for reporting a reward to the        subject, said reward being based on the difference between the        biomarker and a computed threshold Thr_((t));        wherein the memory is connected to the computing unit for        delivering the series of biomarkers and the set of parameters,        and the sensor is connected to the computing unit for delivering        the biomarker;        wherein the computing unit computes an intermediate threshold        Thr_(intermediate(t)) based on the series of biomarkers on a        second time window, such that said intermediate threshold could        provide the subject with an expected reward ratio;        wherein the threshold Thr_((t)) is a weighted sum of the        intermediate threshold Thr_(intermediate(t)) and the threshold        of the previous iteration Thr_((t−1)); and wherein after each        iteration the time windows are moved forward in time.

In one embodiment, the first time window used for obtaining a biomarkeris referred to as the integration time window. According to oneembodiment, the integration time window ranges from 0.1 second to 5seconds, preferably from 0.5 second to 2 seconds. According to oneembodiment, the integration time window is a moving overlapping window.According to one embodiment, the integration time window is a movingnon-overlapping window. According to one embodiment, at each iterationthe integration time window is moved forward in time with a pace ε,preferably a regular pace.

In one embodiment, the second time window used for computing theintermediate threshold Thr_(intermediate(t)) is referred to as theestimation time window. According to one embodiment, the estimation timewindow ranges from few seconds to several tens of seconds, preferablyfrom 2 seconds to 10 seconds. According to one embodiment, theestimation time window is a moving overlapping window. According to oneembodiment, the estimation time window is a moving non-overlappingwindow. According to one embodiment, at each iteration the estimationtime window is moved forward in time with a pace δ, preferably a regularpace. In one embodiment, the pace of the estimation time window δdiffers from the pace of the integration time window ε.

According to one embodiment, the initial threshold Thr₍₀₎ is computedfrom a previous session or from a series of biomarkers computed from abio-signal obtained under a given condition. Said bio-signal obtainedunder a given condition is for instance a resting state bio-signal.

In one embodiment, the reward reported in real-time to the subject isbased on the difference between the current biomarker at time (t) andthe computed threshold Thr_((t)) at time (t).

According to one embodiment, the expected reward ratio ranges from 10%to 100%.

According to one embodiment, the sum of the weighting factors is equalto 1.

According to one embodiment, the computing unit computes the thresholdas follows:Thr _((t)) =α*Thr _((t−1))+(1−α)*Thr _(intermediate(t)); andwherein the memory comprises a coefficient α, said coefficient beingconstant or variable and ranges between 0 and 1. In one embodiment, saidcoefficient ranges strictly between 0 and 1.

Computing the threshold as explained above limits the possible values ofThr_((t)) between bounded values and prevents unwanted growth of thevalues of Thr_((t)).

If α equals to 0, the computed threshold Thr_((t)) matches the movingaverage automated threshold computed on the estimation time windowThr_((t))=Thr_(intermediate(t)). If a equals to 1, the computedthreshold Thr_((t)) reaches a plateau and the threshold at time t equalsthe threshold at time (t−1): Thr_((t))=Thr_((t−1)). The closer α is to1, the more the weight of the threshold's history Thr_((t−1)) increasesleading to computed threshold that takes past values into account. Also,as a increases the threshold progressively converges towards a constantvalue.

According to one embodiment, the coefficient α follows a logistic model,preferably a sigmoid model.

According to one embodiment, the computing unit computes the coefficientα as follows:

${\alpha_{t} = {\alpha_{t - 1} + {{r.\alpha_{t - 1}}*\left( \frac{k - \alpha_{t - 1}}{k} \right)}}};$andwherein the memory comprises a learning coefficient k, a growth rate rand an initial value α₀ of the coefficient α.

According to one embodiment, α₀ ranges from 0 to 1. According to oneembodiment, k ranges from 0 to 1. According to one embodiment, r rangesfrom 0 to 1.

According to one embodiment, the memory further comprises a reward ratiotolerance and a third time window; and wherein the computing unit resetthe coefficient α to its initial value α₀ if the reward ratio computedduring the third time window departs from more than the reward ratiotolerance around the expected reward ratio.

In one embodiment, the third time window used for resetting α isreferred to as the reward ratio tolerance time window. According to oneembodiment, the reward ratio tolerance time window ranges from 0 secondand the session duration, preferably from 10 seconds to 1 minute.

According to one embodiment, the reward ratio tolerance ranges between0% (no tolerance, the threshold resets every time the reward ratiocomputed on the estimation time window is not exactly equal to theexpected reward ratio) and 100% (large tolerance, the reward ratio cantake any value without inducing a threshold reset).

According to one embodiment, the memory further comprises a time gatingand a first reward is reported to the subject by the interacting meansif the subject maintained the biomarker above or below the computedthreshold Thr_((t)) during a time longer than a time gating parameter,depending if the biomarker is trained towards respectively above orbelow the threshold.

According to one embodiment, the time gating is below 1 second,preferably ranging from 100 to 600 milliseconds. According to oneembodiment, the time boosting is longer than the time gating. Accordingto one embodiment, the time boosting is ranging from 1 to 5 seconds.

According to one embodiment, the memory further comprises a timeboosting and a second reward is reported to the subject by theinteracting means if the subject maintained the biomarker above or belowthe computed threshold Thr_((t)) during a time longer than a timeboosting parameter, depending if the biomarker is trained towardsrespectively above or below the threshold.

According to one embodiment, the computing unit further implements thestep of removal of artefacts from the bio-signal of the subject beforecomputing the biomarker representative of the bio-signal on a first timewindow. According to one embodiment, the computing unit furthercomprises a real-time artefact removal algorithms.

According to a preferred embodiment, the bio-signal is obtained usingelectroencephalography.

According to one embodiment, the memory further comprises a series ofbiomarkers of a subject and a series of thresholds manually chosen by anoperator; and the computing unit identifies the optimal set ofparameters that minimize an error function between the manual thresholdand the threshold Thr_((t)) computed from the same series of biomarkerswith the method according to the first aspect of the invention.According to one exemplary embodiment, the error function is a method ofleast squares.

According to one embodiment, the memory further comprises a series ofbiomarkers of a subject and a series of thresholds manually chosen by anoperator during a previous session; and the computing unit identifiesthe optimal set of parameters that minimize an error function betweenthe manual threshold and the threshold Thr_((t)) computed from the sameseries of biomarkers with the method according to the invention.According to one exemplary embodiment, the error function is a method ofleast squares.

In one embodiment, the following parameters may be automaticallyinitialized: the expected reward ratio, the time gating, the timeboosting, the second time window, the reward ratio tolerance, the thirdratio tolerance time window, the growth rate, the learning coefficientand/or the initial value α₀ of the coefficient α.

According to the Applicant, the added value of the present invention isthat it has two behaviours: (i) it follows and is adjusted to thesubject's biomarker—in a way similar to that of moving averagemodels—and, (ii) it can stabilize for given periods of time, whichtemporarily challenges the subject. Hence, it fully allows theoptimization of both the subject's engagement and his challenge.

Moreover, the way the thresholds adapts and stabilizes depends on“parameters”, which can be set to cover a broad range of behaviours. Forinstance, the model can learn quickly and keep adjusting to subject'sevolution, or it can slowly evolve and finally converge to a value.These parameters and the broad range of behaviours they generate coverthe inter-operator variation of practice and make of it a versatiletool, which can be used in several ways:

-   -   the same threshold behaviour is applied to all subjects: for        instance to run a trial where the threshold selection remains        consistent across all subjects;    -   a subject-specific threshold behaviour (more or less        challenging) is applied: for instance to challenge less subjects        who seem to lack self-confidence; and    -   a threshold behaviour derived (learned in the machine learning        standpoint) from the manual threshold of initial sessions in        order to reproduce a given specialist's habit. In said        embodiment, the threshold is initialized on calibrations        recorded at office or at the beginning of every training        session, and its parameters are learned from specialist manual        thresholding on at-office sessions.

In a fourth aspect, this invention aims at proposing a system forimplementing the method according to the second aspect of the invention.Especially, said aspect relates to a system comprising means forcarrying out the method according to the second aspect.

Optionally, the invention aims to provide a computer program comprisinginstructions which, when the program is executed by a computer, causethe computer to carry out the method according to the second aspect ofthe present invention.

In an alternative embodiment, the invention aims to provide acomputer-readable medium comprising instructions which, when executed bya computer, cause the computer to carry out the method of the secondaspect of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the signal processing pipe from raw EEGs to theimplementation of an automatic threshold with the moving average method.The neuromarker is computed from EEG signals recorded on severalchannels. The signal in specific frequency bands is integrated over atime window which is taken every ε seconds (also noted “the integrationtime window”). Said value is averaged over all electrodes, leading to aneuromarker time-series of frequency 1/ε. On said signal an “estimationtime window” is taken every δ seconds and a threshold value is computedfrom it so that the reward ratio on the window equals to the expectedreward ratio.

FIG. 2 illustrates the reward and booster implementation based on theneuromarker activity and the threshold level.

FIG. 3 illustrates the automatic threshold computation over timeaccording to the invention.

FIG. 4 illustrates the automated thresholding computed on an EEG signalaccording to one embodiment of the invention, see example 1. The X-axisrepresents the time indicated in seconds. The Y-axis represents theratio between the signal energy in the range from 4 to 8 Hz and thesignal energy in the range from 12 to 22 Hz.

FIGS. 5A and 5B illustrates the automated thresholding computed on anEEG signal according to another embodiment of the invention, see example2. The X-axis represents the time indicated in seconds. The Y-axisrepresents the ratio between the signal energy in the range from 4 to 8Hz and the signal energy in the range from 12 to 22 Hz.

EXAMPLES

The present invention is further illustrated by the following examples.

Example 1

The present invention was implemented with a set of parameters selectedin order to design a threshold that complies with the following:

-   -   initializes on a calibration session;    -   adapts to data rapidly and relatively constantly;    -   remains stable over time in order to challenge the subject; and    -   resets when the reward percent is too far from the expected        reward ratio for too long.

The chosen parameters are:

-   -   Growth factor r=0.02 —Reward estimation time (s)=30    -   Learning coefficient k=1 —Reward ratio tolerance (%)=15    -   Expected reward ratio (%)=60% —Reward ratio tolerance time        window (s)=30    -   Time gating γ (s)=0.500 —α₀=0.8    -   Time boosting β (s)=2

The result is displayed in FIG. 4. The threshold is constant at thebeginning of the signal, as it has been previously initialized on thecalibration. After approximately 30 seconds in the signal, the thresholdresets, and computes its new value based on the intermediate threshold'svalue computed on the current moving average window and previous valuesof the threshold, according to parameters r and α₀, in order to providerewards 60% of the time. The threshold is then stabilized to its newvalue equal to 0.25 for about 70 seconds before a new reset and athreshold value that decreases and stabilized around 0.2.

Example 2

The present invention was implemented with a different set ofparameters.

-   -   Growth factor r=0.01 —Reward estimation time (s)=30    -   Learning coefficient k=1 —Reward ratio tolerance (%)=15    -   Expected reward ratio (%)=60% —Reward ratio tolerance time        window (s)=30    -   Time gating γ (s)=0.500 —α₀=0.3    -   Time boosting β (s)=2

The result is displayed in FIGS. 5A and 5B. As disclosed, with lowervalues of r, the computed threshold is more adaptive to data but takesmore time to converge toward a constant value, resulting in largeoscillations before the threshold stabilization (FIG. 5A). Likewise,lower values of the initial value of α (i.e. α₀) is associated to anunsteady threshold that oscillates a lot (FIG. 5B).

The invention claimed is:
 1. A system for biofeedback training of asubject, said system comprising: at least one sensor for obtaining abio-signal of the subject; a computing unit comprising a memory, thecomputing unit in communication with the at least one sensor, thecomputing unit configured to: receive the bio-signal of the subjectobtained by the at least one sensor; for each first time window of aplurality of first time windows applied to the received bio-signal at apredefined first pace, calculate one biomarker value based on a portionof the received bio-signal in each first time window of said first timewindows, so as to generate a time-series of biomarker values; for eachsecond time window of a plurality of second time windows applied to thetime-series of biomarker values at a predefined second pace, the secondtime windows corresponding to a computation time t: calculate anintermediate threshold Thr_(intermediate(t)) based on the biomarkervalues comprised in said second time window, said intermediate thresholdThr_(intermedediate(t)) being a reward ratio representative of a storedexpected reward ratio, compute a threshold Thr_((t)) as a weighted sumof the intermediate threshold Thr_(intermediate(t)) and a threshold ofThr_((t−1)), where Thr_((t−1)) is obtained for a previous time windowcorresponding to a computation time t−1, and calculate a differencebetween each biomarker value of the time-series of biomarker values andthe threshold Thr_((t)); and at least one of a display and an auditoryfeedback element, for reporting a reward to the subject, where saidreward is based on said difference between each value of the time-seriesof biomarker values and the threshold Thr_((t)).
 2. The system forbiofeedback training of a subject according to claim 1, wherein a sum ofweighting factors of said weighted sum is equal to
 1. 3. The system forbiofeedback training of a subject according to claim 2, wherein thecomputing unit computes the threshold Thr_((t)) as follows:Thr_((t))=α* Thr_((t−1))+(1−α) * Thr_(intermediate(t)), and wherein α isa coefficient that is one of constant and variable between 0 and
 1. 4.The system for biofeedback training of a subject according to claim 3,wherein the coefficient α is computed according to a logistic model. 5.The system for biofeedback training of a subject according to claim 4,wherein the computing unit computes the coefficient a corresponding to acomputation time t as:${\alpha_{t} = {\alpha_{t - 1} + {{r.\alpha_{t - 1}}*\left( \frac{k - \alpha_{t - 1}}{k} \right)}}};$and wherein the memory has stored therein a learning coefficient k, agrowth rate r and an initial value α₀ of the coefficient α.
 6. Thesystem according to claim 5, wherein the memory also stores a rewardratio tolerance and a third time window, and wherein the computing unitresets the coefficient α to the initial value α₀ if a reward ratiocomputed during the third time window deviates from the stored expectedreward ratio by more than the reward ratio tolerance.
 7. The system forbiofeedback training of a subject according to claim 1, wherein thememory has stored therein a time gating parameter, and wherein a firstreward is reported to the subject by the at least one of a display andan auditory feedback element whenever the subject maintains thebiomarker values above the computed threshold Thr_((t)) for a timelonger than the time gating parameter for an up training protocol, orwhenever the subject maintains the biomarker values below the computedthreshold Thr_((t)) for a time longer than the time gating parameter fora down training protocol.
 8. The system for biofeedback training of asubject according to claim 7, wherein the memory further storescomprises a time boosting parameter and a second reward is reported tothe subject by the at least one of a display and an auditory feedbackelement whenever the subject maintains the biomarker values above thecomputed threshold Thr_((t)) during a time longer than the time boostingparameter for an up training protocol, or whenever the subject maintainsthe biomarker values below the computed threshold Thr_((t)) for a timelonger than said time boosting parameter for a down training protocol.9. The system for biofeedback training of a subject according to claim1, wherein the computing unit is further configured to remove artefactsfrom the bio-signal of the subject before computing the time-series ofbiomarker values.
 10. A method for biofeedback training of a subject,said method comprising: receiving a bio-signal of the subject obtainedby at least one sensor; for each first time window of a plurality offirst time windows applied to the received bio- signal at a predefinedfirst pace, calculating one biomarker value based on a portion ofbio-signal comprised in said first time window, so as to generate atime-series of biomarker values; for each second time window of aplurality of successive second time windows, each second time window ofsaid second time windows corresponding to a computation time t:computing an intermediate threshold Thr_(intermediate(t)) based on theseries of biomarker values comprised in said second time window, saidintermediate threshold Thr_(intermediate(t)) being a reward ratiorepresentative of a stored expected reward ratio, computing a thresholdThr_((t)) as a weighted sum of the intermediate thresholdThr_(intermediate(t)) and a threshold Thr_((t−1)) obtained for aprevious time window corresponding to a computation time t−1, andcalculating a difference between each biomarker value of saidtime-series of biomarker values and the computed threshold Thr_((t));and reporting a reward to the subject via at least one of a display andan auditory feedback element, where said reward is based on saiddifference between each value of the time-series of biomarker values andthe threshold Thr_((t)).
 11. The method for biofeedback training of asubject according to claim 10, wherein a sum of weighting factors ofsaid weighted sum is equal to
 1. 12. The method for biofeedback trainingof a subject according to claim 11, wherein the threshold Thr_((t)) iscomputed as follows:Thr_((t))=α* Thr_((t−1))+(1−α) * Thr_(intermediate(t)), wherein a is acoefficient that is one of constant and variable between 0 and
 1. 13.The method for biofeedback training of a subject according to claim 12,wherein the coefficient a follows is computed according to a logisticmodel.
 14. The method for biofeedback training of a subject according toclaim 13, wherein α corresponding to a computation time t is computed as${\alpha_{t} = {\alpha_{t - 1} + {{r.\alpha_{t - 1}}*\left( \frac{k - \alpha_{t - 1}}{k} \right)}}},$and wherein k is a learning coefficient, and r is a growth rate.
 15. Themethod for biofeedback training of a subject according to claim 12,wherein the coefficient a is reset to a predefined initial value a₀ if areward ratio computed during a third time window departs from the storedexpected reward ratio by more than a stored reward ratio tolerance. 16.The method for biofeedback training of a subject according to claim 10,wherein a first reward is reported to the subject by the at least one ofa display and an auditory feedback element whenever the subjectmaintains the biomarker values above the computed threshold Thr_((t))for a time longer than a time gating parameter for an up trainingprotocol, or whenever the subject maintains the biomarker values belowthe computed threshold Thr_((t)) for a time longer than the time gatingparameter for a down training protocol.
 17. The method for biofeedbacktraining of a subject according to claim 16, wherein a second reward isreported to the subject by the at least one of a display and an auditoryfeedback element whenever the subject maintains the biomarker valuesabove the computed threshold Thr_((t)) during a time longer than a timeboosting parameter for an up training protocol, or whenever the subjectmaintains the biomarker values below the computed threshold Thr_((t))for a time longer than said time boosting parameter for a down trainingprotocol.
 18. The method for biofeedback training of a subject accordingto claim 10, wherein initial values of Thr_((t)) are computed from aprevious session or from a series of biomarkers computed from abio-signal obtained under a given condition.
 19. The method forbiofeedback training of a subject according to claim 10, furthercomprising: removing artefacts from the bio-signal of the subject beforecomputing the time-series of biomarker values.